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SASInstitute SAS Predictive Modeling Using SAS Enterprise Miner 14 Sample Questions:

1. Multicollinearity in regression refers to which of the following?
Response:

A) non-constant variance of the target variable
B) high skewness in distributions of input variables
C) high correlations among input variables
D) non-normality of the target variable

2. Perform these tasks in SAS Enterprise Miner:
- Add a Decision Tree node after the Impute node with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the decision tree to use 1 for Number of Surrogate Rules and Largest for Method in Subtree. Do not change any other property of the Decision Tree node.
- Add another Neural Network node after the decision tree with TARGET as the dependent variable and all other input variables as independent variables (main effects only). Configure the Neural Network model to use Average Error for Model Selection Criterion. Do not change any other property for the Neural Network node. Run the process flow.
The number of parameters (weights) estimated by the Neural Network model is in which of the following ranges?
Response:

A) 11-15
B) 6-10
C) less than or equal to 5
D) 16 or more

3. What percentage of observations in the test data has TARGET=1?
Response:

A) 16.6627
B) 16.8874
C) 83.3333
D) 16.5924

4. Refer to the following profit matrix and confusion matrix for a campaign soliciting product purchases. The predicted variable is a binary outcome.

Based on the above tables, what is the average profit? You may use a calculator for this question. On the certification exam, an on-screen calculator is provided for you.
Select one:
Response:

A) 690
B) 69
C) 6.9
D) 86.25

5. If the bank wanted to select the best model based on which model achieves the highest lift for the third decile in the validation data, then which of the following is the best model?
Response:

A) Decision Tree
B) Regression
C) Neural Network
D) Decision Tree (3-way)

Solutions: